Therefore, our approach allows us to construct a semantic and logical representation of texts via the conceptual graphs.

text comprehension can be defined as a cognitive process of understanding concepts from a text and relationships between them.


Then, we transform these texts into a standard format that can be easily manipulated.

we propose to add the UTF-8 in-coding, to filter the non-Arabic letters, etc.


We found that onset of the rising pitch is perceived to be lower on a high vowel than on a low vowel, but the offsets are perceived to be similar.

the perceived slope of a rising pitch on a high vowel is sharper and more different from a level pitch than on a lower vowel, leading to a shorter duration for its perception.


Given the significant changes across the lifespan reported by the abovementioned real time studies, the validity of the hypothesis and the generality of language variations revealed by "synchronic approach" were challenged (Bailey, 2002).

more real time evidence is needed in our understanding of Cantonese sound changes.


Meanwhile, the minimum values were overall larger than 60 Hz, which were within a male's normal pitch range.

the original data distribution was right skewed: the mean value across all measurement points was around 119 Hz, 23 Hz larger than the median, 96 Hz.


More importantly, the speech rate slightly dropped to 273.79 after 2000, whereas the significant surges were witnessed for both pitch range and level by 8.2 and 7.67 Hz respectively.

inferred from the inconsistency of changing patterns, there is no correlation between the speech rates and the pitch ranges and levels.


This shows that the automatic evaluation by linguistic test points is similar to the automatic translation metrics.

we can identify the strengths and weaknesses of NMTs by building simple <target words> for the automatic evaluation by linguistic test points without having to create a lot of reference sentences.


The relation TOPIC(X, Y) namely means the entity X deals with the topic Y.

the entity X tends to be a research activity, such as "analysis", "survey" and "discussion" etc.


For instances, in Example 3 and Example 4, both target entities "predicted" and "combine" belong to the same entity type, PROCESS, but the former specifically belongs to the TSS, OUTPUT-PROCESS, and the latter does not.

based on this difference, a RC system could easily distinguish them, and classify the former as OUTPUT relation.


We test our hypothesis on different RC tasks in the computational linguistic domain in which some RC task, like SemEval-2018 task 7 (Gábor et al., 2018), aims to classify relations, such as USAGE, TOPIC and MEDOL-FEATURE, and other task, like RC on RANIS dataset (Tateisi et al., 2014), asks for identifying relations such as INPUT and OUTPUT.

we come up with four 2 types of TSS, as shown in the first column of Table 1, for distinguishing these relations for a given specific task.


Previous works have shown that scientific papers specific pre-trained word embeddings can improve training for scientific RC models (Rotsztejn et al., 2018;Hettinger et al., 2018;Jin et al., 2018;Luan et al., 2018).

in this work, we trained the scientific papers specific word embeddings on the ACL Anthology Reference Corpus (Bird et al., 2008) by the skip-gram NN architecture made available by the Gensim word2vec tool.


This indicates that, given a specific RC task, different TSS could have different contribution to the overall performance.

it would be important to select proper TSS for a given RC task.


The GRU equations are formulated as shown below, where e t is the real-valued hidden-state vector at time step t, v t is the corresponding sentence input vector and rev() is the reverse function.

for a document D with m sentences, the bidirectional document encoder's final hidden state, which is the document representation e, is given by, where e f m and e b m are the final hidden states of the forward and backward sentence-level GRU-RNNs respectively, [] represents vector concatenation.


We use 100 dimensional GloVe (Pennington et al., 2014) 2 and we use full vocabulary of 400K uncased tokens which is significantly larger than 150K limit as set by (Nallapati et al., 2017).

our sentence representation is 100 dimensional.


However, several experiments involving various model architectures and word embeddings would be required to really establish this hypothesis.

we leave it as an interesting avenue for future works.


Their model leverages both word-level and character-level knowledge.

we wanted to implement the methods: CRF, LSTM-CRF and checked the effectiveness of handcrafted features on the models.


The relation between a document and the information that presents it is a binary relation (the information presents the document or not).

a concept lattice can easily model the document/term relation and can be considered as an semantic index (Fkih and Omri, 2016).


The experimental result can be justified by a theoretical study.

if we compare the complexity of our algorithm with the complexity of other algorithms, we find that FCA Retrieval is the least complex.


includes adverb 太 tài 'too' and sentence-final particle 拉 la 'SFP' to mark surprise emotion.

adverbs and sentence-final particles in exclamative sentences are efficient to indicate and intensify emotions.


interjections not only mark emotions, but also elicit exclamative sentences to intensify emotions.


However, these studies do all point to the fact that transfer from a typologically similar language (whether L1 or L2) is one of the most influential factors for learning an L3.

there are multiple factors that could be thought to influence the acquisition of L2 French and L3 English by L1 Burkinabe language learners.


According to the previous studies concerning L2 acquisition, age of onset and number of years spent studying a language are among the largest factors influencing the acquisition of syntactical components differing from the L1.

it follows that for L1 Burkinabe speakers, age of onset and number of years spent studying both L2 French and L3 English could have respective effects on both languages.


For these reasons, it is possible that L1 Burkinabe speakers will be aided in their L3 English acquisition by their L2 French abilities.

we summatively pose the following hypotheses: 1.


In our purpose to investigate the preferred patterns of ARGUE, these annotations were too detailed.

we integrated them together with a new label of 'ABS' (as the abbreviation of 'Absence') to represent the absent core FEs.


With the exception of the SVM reaching about the same recall and F-score for the two algorithms, nearly all other values are lower for PV-DM.

pV-DBOW can definitely be seen as more effective for the current dataset, although this is somewhat unexpected, given Le and Mikolov's original results of pV-DM performing consistently better.


The concept of metaphor can be seen as a non-discrete category, with arguably no delineable boundary between the presence or absence of it in a particular environment, depending rather on various factors outlined above, such as concreteness, presence of a historically older and more basic alternative meaning, and so on.

it is not always clear even for a human annotator, meaning that some discrepancy between the manual annotations and the automatic predictions would be expected.


Systems must catch the attention of the driver and make him take the appropriate action.

it is important for systems to communicate with their users in accordance with their individual differences.


A corpus designed for this goal is more critical than anything else, and it must include the following three characteristics: • Colloquial and daily expressions under actual driving experiences, • Subjectivity (opinions, perceptions, cogitations, thinking, and emotions), • Driving behaviors.

for the first step, we construct a Driving Experience Corpus (DEC) in Japanese, which consists of 253 blog articles (7,831 sentences).


Although she did not have any control over the car, she was placed into the trouble due to no choice or fault of her own.

we regarded this extended self as self-behavior.


The discrepancies in the process of constructing the guidelines are quite essential characteristics in Japanese blogs or SNS texts.

our guidelines are also useful to those who prepare blogs and SNS texts in Japanese.


The semantics of the related words after combination are independent of A and B, but they have a new meaning on the basis of original meaning.

from a diachronic point of view, the meaning of related compounds is closer to their new meaning after combination in vector space.


Appropriate use of pronouns and kinship terms include or distance the speaker, audience, and other politicians.

political discourses hold a great importance in the field of Critical Discourse Analysis.


BCCWJ is assigned the Lemma ID for all morpheme entries.

the alignment table enables us to extract all possible word senses by the WLSP article numbers.


In Japanese, the subject word in sentences is often omitted, zeropronouns.

we need to identify the subject word in each sentence.


We assume that a good feature for the trivia score estimation rarely appears in real-world texts because the important point about trivia is a gap between words.

we compute a co-occurrence value, coF req, as follows: where s is the subject extracted Section 4.1.1. w is the pair word with s. pair-f req(s, w) is the cooccurrence frequency of the pair (s, w).


It is not suitable for the estimation model.

we reconstructed the dataset by the following process.


In other words, the range that our method can estimate is insufficient.

we need to discuss new features for identifying trivia sentences with high trivia scores (0.85 to 0.95).


Our final goal is to apply trivia sentences into our dialogue system.

our motivation is to extract 10 10% of 1031 instances.


The final output is obtained by running a softmax function after a similar linear transformation, W 2 , to the hidden vector as follows: where W 2 ∈ R |T |×H 2 , b 2 ∈ R |T | , and y t ∈ R |T | .

b 2 denotes a bias vector, and y t denotes the distribution vector for each possible label.


Representation learning is an actively studied topic in NLP because it overcomes the data sparseness problem.

the same practice is followed to represent each character as a real-valued vector, v c ∈ R d , where d is the dimensionality of the vector space.


However, characterbased JWS models lack word-level information useful in determining the character sequences constituting a word.

a Japanese morphological analyzer typically uses a dictionary.


Preliminary experiments indicate that a window size of 5 is better than others in terms of both accuracy and training time.


A small learning rate (such as 0.01) degrades accuracy and significantly affects learning time.

a learning rate of 0.1 is selected for all the experiments.


This domain comprises of official documents published by the government.

kanji covers a substantial portion of the corpus.


However, they are of the same character type "Hiragana."

they are incorrectly combined to form a fake word.


Today, EncDec and its variants are widely used as the predominant baseline method in these tasks.

the models such as the coverage and the reconstruction cannot work appropriately on abstractive summarization.


As described in Section 3, EncDec calculates a probability distribution over the target vocabulary o j from z j .

encDec with the SPM jointly estimates the probability distributions over the source and target vocabularies from the same vector z j .


Formally, we train the SPM with EncDec by minimizing the following objective function G 2 : Inference In the inference time, the goal is only to search for the best target sequence.

we do not need to compute SPM during the inference.


Similarly, it is also unnecessary to produce pad after generating eos .

the actual computation cost of our method for the standard evaluation is exactly the same as that of the base EncDec.


In addition, we investigate whether the SPM improves the performance of the state-of-the-art method: EncDec+sGate.

we compare the following methods on the same training setting.


In detail, our training data does not contain unk because we adopted the BPE to construct the vocabulary.

our experimental setting is severer than that of previous studies with the presence of unk in the datasets of Gigaword Test (Rush) and Gigaword Test (Zhou).


The motivation of the SPM is to suppress odd-gen by enabling a one-to-one correspondence between the source and the target.

in this section, we investigate whether the SPM reduces odd-gen in comparison to EncDec.


We assume generated headline which is shorter than the gold omits one or more important phrase.

we compute the difference in gold headline length and the generated headline length.


kens for the headline such as "straight" and "tuesday" with pad tokens.

this example suggests that the SPM achieves better token-wise correspondence than attention.


On the other hand, EncDec concentrates most of the attention at the end of the sentence.

most of the target-side tokens are aligned with the sentence period of the source sentence.


In particular, their pointer-generator network is a hybrid between a sequence-to-sequence attention model (Section 2.2) and pointer network (Vinyals et al., 2015).

this network considers the source as well as target word distributions, and thereby addresses the problem of unknown word generation.


In particular, in a sequence summary, the third sentence is based on the second sentence, whereas in a parallel summary, the third sentence is based on the first sentence.

our proposed model uses this characteristic to generate the third sentence in a summary.


In contrast, the score of the sentences in the reverse order is low (e.g., the scores shown in bold in the row of '213' and the second column).

based on the above discussion, it is suggested that the first sentence should be correctly generated in order to evaluate the summary structure appropriately.


Instead of using a declarative sentence to convey sadness, the writer in (5) expresses the emotion by means of a rhetorical question.

the question is neither annotated as the pre-event, nor post-event but the pivot event.


However, if the emotion keyword 興奮 'excited' is not present, the question may not be comprehended as a happiness expression or a happiness expression of such strong intensity.

it would be of value to the implicit emotion identification if one could collect this kind of expressions and study how people use them to convey happiness implicitly, or what kinds of events usually trigger happiness.


The use of 就 implies the meaning that the situation will turn out to be negative to the speaker, but he/she is not capable of preventing it from happening.

it is oftentimes used to express sadness.


Fear is typically triggered when a person thinks that some bad things are going to happen.

a number of questions are formed to seek help.


The values in the dendrogram are not the p-values but the probabilities by which intergroup homogeneity is determined.

in the dendrogram, if A converges with B rather than C, it indicates that the behaviors of (linguistic) factors in A are closer to those in B, rather than those in C. we observed homogeneous group behaviors to predict whether Korean scholars' L2 writings share universal features of translationese.


Seeing that the CIs of three different sub-corpora did not overlap, the factor of TOP-20 HIGH-FREQUENCY WORDS can be also utilized as a TU indicator to classify the types of texts.

the results demonstrate that the QE and KE subcorpora bear the properties of lexical simplification with a lower lexical richness which is not the typicality of native scholars' original texts but the behavior of translated texts.


As Table 2 indicates the behaviors of each factor but not the overall tendency of each sub-corpora, thus, it can be assumed that there might be a possibility that the TU variables may behave similarly among pairs of groups.

we conducted a BP analysis to further investigate specific intergroup homogeneity.


Unfortunately, there is only limited training data of spelling correction in Chinese, and thus it is no easy to train an NMT model achieving good performance of Chinese spelling check.

we want to improve the spelling check model by generating artificial errors to increase training data.


However, lack of edit type annotation makes it difficult to directly identify spelling correction.

we consider consecutive single-character edit pairs of deletion and insertion (e.g., "[-佈-]{+布+}" or "{+布+}[-佈-]") as spelling correction, and extract the sentences containing such edit pairs.


If we use the character-level confusion set, an error-free sentence would produce numerous and probably unrealistic artificial misspelled sentences.

we decide to use word-level confusion sets.


As previous research mentioned above, few studies have aimed at spelling and grammatical error corrections on Japanese functional expressions.

our paper is an attempt to do this work using neural machine translation.


Moreover, learning variables also affect the accent of new and similar vowels in Hong Kong Cantonese produced by non-native speakers.

this study explores the factors that influence the production of the new and similar vowels through having native speakers listen to the Pakistani children speak Cantonese and by the correlation of learning factors and accent ratings.


The results of this project attests whether SLM could account for the sound acquisition of L3, which extends SLM to the field of L3/multilingual research.

the findings from the study would contribute to L3/multilingual acquisition studies and serve as a reference point for Chinese language teaching targeted at NCS students at Hong Kong.


Furthermore, distinct from prior studies, all of the subjects in the current project were L3 learners who had learned English prior to Cantonese at the time of task.

the phonological similarity among Cantonese, English and Urdu typologies could also explain the accent results.


By analyzing these mul-tilingual opinion texts, there can be many useful applications, such as revealing the cultural variations and conducting customer surveys in different areas.

it's necessary to develop an effective MSA model that can process multilingual texts simultaneously.


As we all know, the closer the angle of a word pair is to 90 degrees, the more irrelevant the word pair is.

this means that the two vectors of a word pair from separately trained word embeddings has a large difference.


It is of great significance to quantify the correspondence between the Chinese and the Korean pronunciations of Chinese characters at the syllable level because: first, it documents the contemporary pronunciations of Chinese characters in the two languages to understand their syllabic phonological correlations, which have not been previously examined; second, it serves as an important guideline for curriculum design and learning strategies for teaching and learning Korean as an L2, especially for L1 Chinese learners; third, such quantified relations can be used for the psycholinguistic research concerning word reading and processing in L2 Korean.

the present study aims to answer the following research questions: 1.


Thus, objectively defining the level of text simplicity is difficult.

we focus on the extent of the vocabulary used in a text.


However, no knowledge is available on the most robust model that works for rare words.

to determine a robust system in this task, we compare the machine translation approach and lexical substitution by using the paraphrasing dictionary in the evaluation dataset containing rare words.


As semantic similarity does not distinguish the senses of polysemous words, considering only the semantic similarity between the original and candidate word may lead to selecting a synonym of the wrong sense.

we compute this feature by averaging the semantic similarities between a substitution candidate and each word based on the context of the original word.


This vocabulary size also includes words, such as proper nouns and symbols.

the vocabulary size of the simplified version is more than 2,000 words.


These scores indicate that the outputs of "EncDecAttn" are not clear and cannot preserve the meaning of the original sentence.

the RARE dataset is a critical defect for practical text simplification systems to generate a sentence that differ from the meaning of the original sentence.


In addition,very few changes were witnessed with regard to "LexSub" in both the datasets.

we believe that the candidates that fit the context of the original sentences are not generated.


In this case, if the bilinear term has symmetry, f (a, b) = f (b, a).

this is not appropriate for expression of a directed edge.


This method assumes that the weight matrices of the bilinear terms are symmetric matrices and thus are diagonalizable.

we can transform the bilinear term of the score functions into a "triple inner product" of two input vectors and a weight vector.


We update "frequency" domain (complex space) vectors using only the operations which have correspondence to "time domain" vectors.

as described in Hayashi and Shimbo (2017), the conjugate symmetry of vectors are kept while learning because their initial values satisfy it.


On the other hand, a low SD indicates that the text length of the child elements are roughly equal.

the main content is distributed more evenly among the elements.


Meanwhile, elements with different depth level may not represent related content even if they share the same class name.

we concatenate the class name and depth level to form the key ID (which is the id variable defined at line 6 in Algorithm 2).


A general tree for multiple sentences (e.g., paragraph, document) cannot be generated.

they cannot quantify relationships among sentences.


Homographs are words that share the same written form as other words but have a different meaning, and can be classified into either homonyms or heteronyms, depending on their pronunciation, e.g., homonyms have the same pronunciation, such as lie (untruth) [lÁi] and lie (to recline) [lÁi], whereas heteronyms have different pronunciation, such as desert (region) [déz@rt] and desert (to leave) [diz'@:rt].

disambiguation of heteronyms is a more important task in TTS synthesis, as the correct pronunciation of the word in its context must be determined.


8 The functional morphemes are annotated in CoNLL2009 not as features but as semantic role tags (i.e., target of disambiguations).

the semantic role tags in the Japanese corpus of CoNLL 2009 might be different from those for English SRL.


between main and subordinate clauses, e.g., Cooccurrence and Sequence; however, these similar semantic role tags are not connected to predicate concepts, such as frames, as defined in PropBank or FrameNet.

we do not use EDR as the target SRL corpus.


Unlike the EDR corpus, the JFN (Ohara et al., 2003) was constructed in the same structure of English FrameNet, however, JFN is a closed corpus, and several essential information such as total amount of the annotated sentences and number of lexicons are not revealed.

it does not seem to be easy to use the JFN corpus.


Previous work has shown that neural-networkbased approaches models are powerful for English SRL (He et al., 2017) as well as Japanese case-marker disambiguation and anaphora detection (Ouchi et al., 2015;Shibata et al., 2016;Ouchi et al., 2017;Matsubayashi and Inui, 2018); however, neural-network-based models have various tuning parameters such as network structure, hyper parameters, and several methods of avoiding over fitting.

it is not easy to determine the cause-and-effect relations between models and the final results.


Therefore, it is not easy to determine the cause-and-effect relations between models and the final results.

we used simple neuralnetwork architectures to focus on clarifying what input features are effective, which learning models are powerful, and how different labeled corpora can be effective for the target label set.


As described above, several corpora annotated with different sets of semantic role labels for Japanese have been constructed, but the amount of labeled examples for each annotated corpus is limited.

we applied transfer learning, which uses corpora annotated with different semantic labels from the target labels.


In transfer learning, all the units in the final layer for GDA tags are discarded; however, some of the tags are almost the same as the semantic role tags defined in BCCWJ-PT, such as Agent, Theme, and Goal.

we must consider how we can use the similar semantic tags in transfer learning.


Syntactic features are powerful; however, parse errors will decrease the accuracies of SRL systems.

zhou and Xu (2015) proposed an end-toend SRL model using bi-directional long shortterm memory (LSTM) without any syntactic features.


These studies are focused on anaphora resolution, i.e., detecting arguments for a predicate without dependency relations and recognizing their semantic roles.

these studies discussed how to incorporate the effectiveness of multiple predicates.


Intuitively speaking, the TPS of a given dependency type in a given corpus indicates the average occurrence of that dependency type in a sentence in the same corpus.

it is expected that the TPS of the same dependency type can vary across corpora of different genres.


The possible correlation between amod and compound can be considered a natural result when we take the functions of these two dependency types into consideration; both share the function of modifying nouns, and, if a writer intends to modify more nouns in the text in some way, adjectives and noun compounds are usually able to meet this requirement.

the writer will develop a tendency to use more adjectives and noun compounds.


(Ratinov and Roth, 2009) has shown that using a more expressive tagging scheme like IOBES improves the model performance marginally.

this could be a possible reason for why our model's performance is behind both (Lample et al., 2016) and (Ma and Hovy, 2016).


Possible reasons for this observation could be, lack of training observation for Miscellaneous entity (refer Table  2) and the absence of common patterns in Miscellaneous entities and words inside the name of Location entities.

we can conclude that identifying Miscellaneous entities is much more challenging compared to other type of named entities.


At the same time, the amount of patterns need to not be too large that might overwhelm a user or subsequent language assessment systems.

our goal is to return a reasonable-sized set of patterns and cover typical usage of a given word.


In particular, Vietnamese syllable is a combination of consonants, vowels, and tone.

switching each of these components between two syllables are likely to make spoonerism.


Vietnamese people often use spoonerism to describe things in vulgar way while our language model is built from formal texts.

we create a dictionary which consists Vietnamese vulgar word from Internet to improve the performance of our language model.


As discussed in section 3, we use language model to calculate the score of each Type 1 spoonerism candidate and then choose candidates whose scores are greater than the threshold.

we use the development set to find the optimized thresholds.


In this case, we suggest that the BLSTM model, especially stacked BLSTM, does not need explicit context information such as neighboring word embeddings.

by only using WE + POS as the features, combined with DBLSTM, the model can achieve compelling result.


Few studies have explored the oral production, which is a kind of instantaneous task where more attention is paid to semantics and pragmatics.

the current study aims to explore the transfer and unlearning of TP features by Chinese learners of English by analyzing TCCs in their oral and written English.


Interaction effects on the frequency of use were only found between the type and the production forms (F = 8.325, p < 0.001).

the multivariate ANOVA was carried out to examine whether the proficiency level and the production forms decidedly affect the incidences of each types of TCC.


The OC texts contain QA texts like dialogue.

a hearer (exo1) tends to fill the NOM case of the predicate as in "oshiete (tell)" in the first example.


However, the features extracted by simple RNN and CNN are still not sensitive to location information which may miss core semantics.

the attention mechanism has been designed to solve this problem.


Similar to traditional feature-based approaches, such methods could lead to limitation on performance due to confined linguistic knowledges.

an alternative is to use sentence-level embedding to represent overall features of target text instead of handcrafted features set, together with syntactic dependency trees (Yu el al 2014) or Ranking CNN (Santos et al.


Actually, features extracted by both CNN and RNN are, in some way, compatible and unique.

simply combine both CNNs and RNNs outputs have been proved improvement in relational classification task (Nguyen and Grishman 2015).


As shown in Table 3, the performance dropped after we applied these two constraints, which indicated that the network had already paid attention to these words without using the attention mechanism, and effectively used them for classification.


When we conduct learning and testing on the space W , a domain shift does not occur.

the manner in which to construct W becomes the problem.


Furthermore, there are several methods to embed documents in the lowdimensional space, but there is no single embedding algorithm that can be used to respond to various domain adaptations from the diversity of domain differences.

we combine the embeddings of the document in this study, specifically the combination of doc2vec (Lau and Baldwin, 2016) and singular value decomposition (SVD).


In this study, the target task is sentiment analysis: therefore, the data is a document.

we can construct a shared feature subspace by using the embedding for the document.


The method constructing embedding for the document can also be seen as the dimension reduction of the document vector represented by the BOW model.

the shared feature subspace can also be constructed using the SVD.


That is, the perceived severity of the symptoms was not clear cut.

they were not included in the experiment.


Analyses: Twenty-seven participants who rated any two or three of the three fillers with either end where such rating was impossible were excluded from the analyses.

the analyses included data from 107 participants.


Even though the test items were categorized as either perceived-to-be-serious type or perceivedto-be-unserious type, the items in each type were still different from one another in terms of the degree of perceived severity.

to capture the effect of such difference, the degree of perceived-severity which were obtained from the stimulus norming was also included as a fixed factor.


By comparing the results of both types of situations, it can be seen that participants paid less attention to possibility hedges when the situations turned out to be better than expected.

first this present study showed that not only in the narrative context but also in the non-narrative one that the effect of expectation on the processing of unexpected texts can be observed.


First, we transform the word vectors for each symptom category to a single vector by element-wise multiplication.

we generate a symptom matrix containing representative vectors for each category.


We focus on the order of words among other styles and use the distribution of part-of-speech tags.

we input a sequence of part-of-speech tags consisting of a post to the network.


We note that it is necessary to consider feature weights prior to combining the feature networks because each post shows a different level of depressive characteristics.

we generate a vector with weights indicating which feature is most representative in analyzing the post for classifying the user.


For this reason, when the experts predict depression, they focus on some particular posts which clearly show depressive characteristics distinct from other posts.

we believe that it is necessary to appropriately select and process such posts according to their importance.


We simplified the model by excluding a CNN for the character-level representations.

this model consists of three layers: bidirectional LSTM, linear, and linear-chain CRF layers.


She found out that local languages in their province have very limited space in formal education especially in secondary level.

the preferential use of English in the school LL, on one side, underscores the power, prestige of the language over the national and local languages and other, it may be contingent on the context where the dominant language is used (Astillero, 2011, p12).


However, in practice, current computational models for automatic fake news detection cannot be used yet now due to the performance limitation.

at the present, manual or partially automatic verification is a practical solution.


As mentioned above, in real-world situations, we have to select the claims to be verified from a vast amount of texts.

the automation of this procedure is desired for practical fact verification.


Although full automation of them is ideal, it is not realistic at present due to the low performance for fact verification.

in this work, we aim to realize partial automation to support human fact-checking experts.


Specifically, for a non-agentive argument to appear as the subject, the verb phrase must describe an event that is typically associated with a particular type of agent with expertise (e.g., surgeons, barbers, etc.).

i assume that this conventional association is stored as part of our encyclopedic knowledge, which is invoked when a particular set of lexical items are combined to form a verbal predicate.


Consider (8) and 11 On the assumption made above that -se is an exponent of an applicative head that introduces a recipient argument, the subject arguments in (20) and (21) are introduced as underspecified: the one in (20) can be interpreted agentively or nonagentively, but the one in (21) can only be interpreted agentively in the presence of a recipient argument.

the possibility of co-occurrence of an agent and a non-agent can be reduced to the availability of the applicative morpheme -se.


No such morphological problem arises in the present account: the head introducing a thematically underspecified argument is realized as Ø, while the head introducing a recipient argument (Appl) is realized as -se.

we can eliminate accidental homophony and achieve a consistent morphological analysis under our assumptions.


This is because, as we saw above, -rare is the head responsible for suppression of an external argument and is distinct from the head introducing a thematically underspecified argument.

(22) suggests that heads with different functions can co-occur.


Furthermore, in the presence of a proto-patient, as in (11), the underspecified argument can only be interpreted as a proto-agent.

the interpretational variability can be well accounted for in terms of the interpretive procedure presented here.


The sentence (5a) is ambiguous for xuei35 may either encode a deontic interpretation in Reading 1 or an epistemic interpretation in Reading 2.

when the negation pu55 occurs in the sentence (5b), it has two contrastive readings.


Furthermore, if the assumption is right, it follows that it is preposed as a predicate in (8b) and confined to an animate subject in (11) by virtue of the properties of a directional verb .

far, we have distinguished the final qu and k'ɯ.


In this sense, the final particle in CP layer echoes with the relevant constituents in different layers, say, xuei55 in IP zone, the degree marker in vP zone.

the syntax of the particle k'ɯ2 is read off only if the degreeP and ModP are overtly realized in vP layer and IP layer respectively.


However, Stanford CoreNLP highly depends on the use of periods as end-of-sentence markers.

a post written in the form of a list (e.g.


Further analysis of the posts show users commonly split a single activity or event into different posts as they constantly post updates regarding their status.

instead of classifying a post in isolation, collective classification strategies should be investigated to take advantage of this noticeable dependency between the new post to previous posts.


The rate of articulation does not reflect how much linguistic information, especially meaning as expressed by propositions, is conveyed or represented in a linguistic unit like a syllable.

myers, Tsay, and Su (2011) propose that in order to capture a complete picture of communicative efficiency, both representation efficiency and transmission efficiency should be taken into consideration.


By contrast, representation efficiency reflects a language's structural complexity and hence might show the process of children's development in grammatical knowledge.

the current study investigates Mandarin speaking children's communicative efficiency in comparison with that of the adults.


For English, we observe that while both the character and syllable based inputs perform better than word level input for the MR dataset, word based input performs the best for TREC-En.

we find that, the helpfulness of sub-word level or morphological information in English is inconclusive.


It can be observed from Table 4 that the multiInput-CNN model performs best with a combination of words and syl-ngrams for Hindi and Telugu language datasets, while a combination of word and char-ngrams performed best for the English language datasets.

as a future work, we can experiment with attention based models, which can choose the best combination of inputs for the multiInput-CNN model.


Furthermore, it summarizes the target into specified classes such as positive, negative, neutral, interesting, or bad.

sentimental analysis helps a business to roughly estimate user opinions of their brand, products, or services using online reviews and conversations.


Particularly, in the aforementioned examples, the price and quality are extracted as contextual information for the sentiment word high .

the polarity depends on the contextual information instead of the sentiment word.


Each of these three scores ranges from 0.0 to 1.0 and their sum is 1.0 for each synset.

each synset contains more than one kind of sentiment.


The main purpose of this dictionary is to consider ambiguous words, and this can be accomplished by extracting contextual information from each sentiment word.

understanding the relationship between all words in a sentence is necessary for extraction.


Therefore, understanding the relationship between all words in a sentence is necessary for extraction.

the dependency parser VnCoreNLP by Vu et al.


For manual checking, as the number of mistakes depend on the effectiveness of the dependency parser, the greater the number of mistakes made by the parser, the greater the number of incorrect extracted words.

if the performance of the dependency parser can be improved, a manual check will be increasingly unnecessary.


Therefore, most neutral reviews, even though in reference to only bad sites of products or services, were neither positive nor too bad to be negative.

two types of cases were used in our experiment: all reviews as well as neutral and non-neutral reviews.


However, applying the lexicon to a crossdomain is still possible.

we can see that words that product purchasers use to give their opinion about different types of products and services have many similar points.


Similarly, the problem that occurred when analyzing tiki.vn reviews, ambiguous words were not considered.


Although the overall score was somewhat the same, the false predictions of the opposite class were fewer.

we observed that the ambiguous class was necessary for the dictionary to be usable in practical applications instead of assigning the class to a fixed value.


The inner structures might play a crucial rule in rendering the various distribution and can further account for its syntactic behaviors across Speech and Writing.

this paper aims to re-examine adverbial clauses in terms of syntactic complexity through the investigation on the internal structure variations across speech and writing, with a refined data analysis and a statistical test.


Only with the measurement of both lexical and structural variations can we better index language complexity.

the "STTR of A,CL" basically implies that the speech mode is structurally more varied than the writing mode, despite that the distribution proportion of adverbial clauses in writing is higher than in speech, and this finding conforms to Halliday's claim of speech being structural complex.


And this part is statistically important and empirically necessary since it can provide more persuasive evidence for the investigation of the adverbial clausal behavior in different complex language systems.

in this section, the Welch independent t-test is conducted to find out the statistical difference of adverbial clauses between speech and writing.


This characteristic is thus applicable to other syntactic structures.

the rule of standard syllable units does not have salient effects on haplology.


This significant semantic aspect is not taken into consideration in Wu (2012a).

a re-examination of yuánlái2 is called for.


Although Sorace (2000) suggests this is so, there is, however, little empirical evidence to show this.

this paper aims to examine to what degree the core-peripheral distinction for split intransitivity is cross-linguistically consistent and if it is possible for L2 learners of English to acquire the distinction at the syntax-lexicon interface.


Telicity (goal-direcedness) is the primary feature that can separate the unaccusatives from the unergatives, with "telic change" at the core of unaccusativity.

verbs of change of location, such as arrive and fall are mostly inherently telic and therefore exhibit more consistent unaccusative behavior than verbs of change of state such as die and happen, which do not encode delimitedness.


Finally, because of the telicity restriction, peripheral verbs in the middle of the SIH and unergative verbs are much more restricted in the construction as shown in (5).

(5) *the existed problem, *the sweated man, *the run man the different behaviors of core and peripheral verbs in the PPP construction provides evidence for the gradience of English split intransitivity.


Given the correlation of CO construction as well as PPP construction with the SIH, it should then follow that split intransitivity will also manifest gradience in English and its acquisition as an L2.

this paper poses the following research questions.


The zero anaphora resolution is one of the active research areas in the Japanese language processing as it is crucial for improving the performance of various natural language processing applications such as automatic text summarisation (Yamada et al., 2017), information extraction (Sudo et al., 2001) and machine translation (Kudo et al., 2014).

it has been extensively studied as an urgent problem to be solved (Sasano and Kurohashi, 2011;Hangyo et al., 2013;Ouchi et al., 2017;Matsubayashi and Inui, 2017).


The computational complexity of the training is not realistic.

we prepared another simple candidate reduction method.


In spite of the various articulations due to the following consonant, the meaning of such utterances does not change.

it is difficult for Japanese listeners to identify the two nasal codas.


(2014) also indicated that learners will show the most significant benefit from training if training is designed to take advantage of their individual abilities.

more paradigms of training should be considered.


We selected nine pairs of nasal codas whose preceding vowel is " a ", " e " or " i " with four tones as training stimuli respectively.

the total number of stimuli is 4 (talker) * 4 (tone) * 3 (vowel) * 2 (type: n/ng) * 9 (item) = 864.


A thorough study of the Chinese writing system can thus shed light on the way in which basic concepts were formed when the ancestors of the Modern Chinese people created the characters, and can also provide insights into the possible relations among the concepts.

based on the 540 radicals in Shuowen Jiezi 1 (Xu,121) and the framework of the Suggested Upper Merged Ontology (SUMO) (Niles and Pease, 2001), Chou (2005) and Chou and Huang (2010) constructed Hantology (hanzi ontology), a system pertaining to the relation of Chinese characters and their meaning clusters.


According to Harpe (2015), aggregated rating scale data such as the data we collected can be treated as continuous data; he further advocates that more advanced statistical models would be more powerful than a simple group comparison via t-tests or analysis of variance tests (ANOVA).

a linear relationship was assumed, and linear mixedeffects modelling was applied in our analyses with the 'lme4' package (Bates et al., 2015) in R (R Core Team, 2018).


Trouble talk was critical to the enactment of social support (Goldsmith, 2004) because people sympathized or empathized with the support seekers.

the support seekers could still request and receive social support through self-disclosing without a verbal or explicit request.


The hypothesis of two structurally distinct subject positions in an articulated clause structure has been employed to explain cross-linguistic variations in subject properties in Guilfoyle, Hung and Travis (1992) as well.

the answer to why the A-scrambled non-subject in SpTP controls only certain subject properties could be related to the distinction between high/Pivot and low/PA subject properties in Korean.


Only the variation of rising contour shapes constrained by sex could be a reliable index of anatomical variation.

a series of mixed models were conducted on the slope of low rising tones.


Comparing the results of Wuxi and Songjiang, only ST-AvgF0, ST-xH, ST-, and ST-xmax show better performance than the baseline in both regions.

it appears that the best method for tone normalization will be one of these.


the language they use in such lies might not be the same as lies in real life situation.


They can collect true and deceptive statements at the same time through different modes, such as emails, recordings, face-to-face communication, or handwritten texts.

this study employs controlled experiment to collect data.


This is understandable because in this experiment, when participants were lying, they still talked about people that they really know.

the language they use for objective description about the teachers they dislike is still true.


On these three factors, truthful texts have higher factor scores than deceptive texts and the differences are significant.

these three factors are capable of distinguishing lies from truth in this study.


Finally, the documents are ranked in descending order based on the probability assigned to the query.

if terms of a document lead to higher generation probabilities, the more relevant this document is to the query.


the parent node of the PP-nodes is a node that also holds the conjunction.

in this case the algorithm does not take the sibling of the PP-node but it takes a sibling of the parent node of the PP-node.


A common belief is that they are written in a colloquial style, but [2] shows that the language of these texts is not restricted to the more informal levels of expression and a large number of different genres are involved.

free expressions, literary prose and newspaper writing coexist without a clear predominance.


The first one is the Question Analysis in which the language object of the study is determined using dictionaries with the criterion of selecting the language for which more words are found.

the question type is selected using a set of regular expressions and the keywords of each question are obtained with morphological and dependencies analysis.


Questions 15,16,18,19 and 20 contain both factual as well as opinion aspects and the OQA system performed better than the TQA, although in some cases, answers were lost due to the artificial boosting of the queries containing NEs of the EAT.

it is obvious that an extra method for answer ranking should be used, as Answer Validation techniques using Textual Entailment.


The most relevant properties produced by the subjects in the feature production experiments are in the categories presented above.

asked to list the defining properties of the concepts representing concrete objects subjects will typically: classify the objects (Superordinate), list their parts and the stuff they are made from (Parts and Stuff), specify the location the objects are typically found in (Location), their intended functions, and their typical behavior (Action), or name their perceptual qualities (Quality).


We judge concepts as similar if they are classified under the same node in the taxonomy and we assume that the relevant knowledge of similar concepts is stated in using the same lexico-syntactic patterns.

one expects the patterns expressing knowledge of these concepts to appear in the extracted Wikipedia sentences for more than one concept.


Basque is an agglutinative language, and it presents a high power to generate inflected word-forms.. Verbs offer a lot of grammatical information, as each verb form conveys information about the subject, the two objects, as well as the tense and aspect.

of this wealth of information contained within word-forms, complex structures have to be built to represent complete morphological information at word level.


One of the assumptions usually made implicitly in the design of single-document summarization systems, is that the source document does not contain redundancy.

there is no risk of including a sentence in the summary which does not contain any information not already present.


Since redundancy implies 'relatedness', I regard a redundancy graph a special case of a relevance graph.

δ r is not only included in P r but also in ∆ q,c,r .


A post-mortem analysis of the weaknesses of our original system revealed the importance of text structure for automatic summarization, even in the case of short texts like news stories.

we will only focus on the "Update task" in this paper.


We assume that, for multi-document summarization, redundant pieces of information are the most important elements to produce a good summary.

the sentences which carry those pieces of information have to be extracted.


For example, the word schnabbel (a gig) is analysed as a singular noun, a base adjective and a rst person singular present verb.


We also take into account occurrences of the unknown word in dierent contexts to extract additional features, including the type(s) assigned by the Alpino POS tagger (Prins and van Noord, 2001) and types which Alpino allows as plausible in the particular context.

both the morphology of the unknown word and its context are considered in the prediction process.


If so, there is also no way to know if the root form ends in -s or if s is a replacement for a -z.

for almost each adjective three analyses will be delivered boost (angry): *boo, *boos and booz.


For example, we have omklemd (to clasp) but there is also omgekanteld (to tip over).

both *omkanteld and omgekanteld are generated.


In the latter case, there are enough hits for the generated plural form *schellens because schellen happens to be a family name and schellens is its genitive form Schellens methode (Schellen's method).


Thus, the candidate list to be substituted with erroneous nodes in this method is much larger than previous one.

both the time and performance are much higher than n-best correction heuristic.


Synonymic resources for French are still limited in scope, quality and/or availability.

the French WordNet (Frewn) created within the EuroWordNet project [16] has limited scope (3 777 verbs and 14 618 nouns vs. 7 384 verbs and 42 849 nouns in the morphological lexicon for French Morphalou) and has not been widely used mainly due to licensing issues.


The synonym selection is then done with respect to a verb entry i.e., with respect to either a reflexive or a non reflexive usage.

similarity measures were applied between the definitions of verbs corresponding to the same type of usage.


Although their coverage is large, these resources can't obviously cover all domains in depth.

it can also be interesting to rely on resources whose relations are less well defined but that can be easily extended to a new domain in an unsupervised way from a representative corpus.


A large repository of topics doesn't exist and similarly to semantic resources, it couldn't cover all domains.

[9] performs the discovering of the topics of a text in an unsupervised way and its method can be qualified as endogenous as it doesn't rely on any external resource.


Finally, the selection of topic shifts is performed by applying a threshold computed from the distribution of minimum scores.

a minimum m is kept as a topic shift if score(m) > µ − α • σ, where µ is the average of minimum scores, σ their standard deviation and α is a modulator (α = 0.6 in all our experiments).


These topics are identified in an unsupervised way by clustering the words of the text according to their co-occurrents in this text.

each of its topic is represented by a subset of its vocabulary.


For English, the use of lexical co-occurrences is less effective than for French.

a significant part of F06CT's results for English can certainly be explained by its endogenous approach while for French, the dominant part in F06CT's results seems rather come from its exogenous approach.


This can be partly explained by the differences we have observed in our evaluations between French and English: almost all works are dedicated to English, a language in which word recurrence seems to be particularly effective for topic segmentation.

the use of external resources is not considered as a priority.


Modulo differences in the probability of actions, our measures of informativeness and relevance gave equal weight to the different events mentioned, without reference to context, whereby an event can become relevant not through its association with a potential target action, but because it can shed light on the nature and provenance of the rest of the events described in the text.

when we focus on those events which are indirectly linked to possible actions, BT-45 does not seem to differ very much from the H texts, but this should be interpreted in light of the fact that the two texts did differ on overall informativeness, as reflected by the NE measure.


[21] use a similar bootstrapping approach for information extraction.

using crfs has become the dominant approach to ner [15], allowing for effective feature construction, handling very large feature sets, and modeling complex interactions across multiple levels of granularity; in the present paper, crfs will be our learning method of choice.


Moving to State S6 is triggered by the word the, which has neither backward nor forward dependencies; however, it is linked through a chain of dependencies with a future word board.

we apply the composite TRFC operator to type raise the current word to the required dependency category and then perform forward composition.


For example [13] found only 45 percent parallel text pairs on the multilingual parallel web site Hallå Norden (Hello Scandinavia) that was intended to be completely parallel and the parallel pages contained 5 percent nonparallel elements.

we found a need to develop and evaluate a new method for identifying parallel and non-parallel texts in corpora covering different language pairs.


Usual POS taggers and text chunkers decide the tag of each word by using the features generated from the word and its surrounding words.

similar rules, for example, that consist of the same set of words but only differ in locations from current words, are generated.


Usual POS taggers and text chunkers decide the tag of each word by using features generated from the current word and its surrounding words.

each word and its attributes, such as character-types, are evaluated several times in different relative locations from current word.


Our basic underlying hypothesis is that the salience of an entity e i should be calculated by taking into consideration both its confidence and the confidence of other entities {e j } connected to it, which is inspired by PageRank [13].

for each entity e i , we construct a set of related entities as follows: {n j | n j is a name, n j and e i are coreferential or linked by a relation; and n j is involved in an event mention} Then we compute the salience of e i based on local confidence lc by the baseline single-document event extraction, and select the top-ranked entities as centroid entities: Any extraction errors from the baseline system, especially on name and time arguments, will be compounded in our new cross-document IE task because they are vital to centroid detection and temporal ordering.


If MSTParser is trained on NEGRA excluding the test set -the rest of NEGRA lacking consistence mentioned above -it performs even better, attaching 80.4% of PP-s correctly.

mSTParser as a statistical parser trained on a full corpus becomes a strong competitor for a PP attacher that has been trained on restricted four-tuples input.


This worsening effect can be attributed to the lower values of the WCDG constraints for the corresponding labels and edges than for the MSTParser predictor.

the search could not find a decision scoring better than that when the MSTParser prediction has been followed.


The number of non-projective edges was determined by counting how often an edge crosses some other edge.

if a non-projective edge crossed three other edges the number of non-projective edges equals three.


The χ 2 -statistic is then computed by Similar to IG OAA, this is performed to rank the features ϕ(x j ) for every transition L separately.


Syntactic annotations in the corpus provide information about major constituents (including adjectival phrases) but grammatical functions are indicated only for phrases related to verbs.

no distinction between argumental and non-argumental dependents of adjectives is made in the treebank.


1 We adopt the notion of the subject also for attributive adjectives: the modified noun is a semantic argument of the adjective and can be considered its semantic subject.

we consider that the subject is present in both predicative (2) and attributive (3) uses of an adjective: (2) predicative use:  Corpus annotations indicate adjectival phrases (AP) but they do not specify arguments of adjectives: functional annotations are absent within APs.


Moreover, an argument of an adjective can be outside of an AP, for example the subject of a predicative adjective (2).

we use linguistic knowledge, applied to corpus annotations, in order to identify predicateargument structure of adjectives.


However, building large and rich enough predicate models for broad-coverage semantic processing takes a great deal of expensive manual effort involving large research groups during long periods of development.

the coverage of currently available predicate-argument resources is still unsatisfactory.


In fact, FSI and ASI always provide an interpretation of W. While FSI includes in I the sense having minimal cumulated distance to the first senses of the rest of words in W, ASI includes in I the sense having minimal cumulated distance to the all the senses of the rest of words in W. The rationale behind the FSI algorithm is that the most frequent sense for a word, according to the WN sense ranking is very competitive in WSD tasks, and it is extremely hard to improve upon even slightly [18].

this algorithm expects that the first sense in WN will be correct for most of the words in W. Regarding ASI, this algorithm expects that the words in W (corresponding to a very close semantic field) will establish many close path connections between different synsets of the same word (because of the fine-grained sense distinction of WN).


Doing so in multiple languages simultaneously is thus tantamount to extract "multilingual collocations."

the whole alignment process can heavily be simplified by assimilating a multilingual input corpus to a monolingual one.


Our method does not permit this; instead, the complete phrase "loud applause" is considered to be a translation of the phrase "vifs applaudissements" as a whole.

where [9] computed the average of relative word frequencies for those words that link together, we actually compute the maximum of relative word frequencies for all possible links, i.e., from all "source" words to all "target" words.


Although both corpora consist of news articles and have a similar TE distribution, English and Spanish results are not strictly comparable due to the difference in size of the corpora.

prior to analyzing the results obtained for different languages, we studied the comparability of the results.


Only if this mapping is successful can the thematic relations in visual context provide relevant information for the dependency assignment to a given dependant-regent word pair.

the first step in cross-modal interaction must be the mapping of linguistic entities onto sets of concept instances in visual context.


However, the diversity of nlp tasks that are based on corpora is proportional to the variousness of types of the latter.

many corpora types have been distinguished [9], mainly monolingual corpora, parallel corpora and comparable corpora.


Our bimodal terminology extraction system is based on a hybrid approach, combining both linguistic and statistical features.

the main component of the system is statistical, enriched with shallow linguistic information.


However, Jaccard coefficient does not show the rate of the actual keywords in the overlap between spoken/written keywords.

an additional similarity coefficient was implemented, which is based on Jaccard index and considers the golden standard.


Each pair of written/spoken texts was cleaned from the data that might be noisy for our evaluation.

the references section was removed from the written text due to the lack of relevant information (e.g.


4 shows the results of the comparison of written and spoken keywords, where the average is 18% using Jaccard coefficient and 8% using KSim coefficient.

the percentage of actual keywords in the intersection is 44.5%.


Instead, the learner uses the training instances directly.

training consists in storing the instances in an instance base, and classification finds the k nearest neighbors in the instance base and chooses their most frequent class as the class for the new instance.


In order to obtain such behavior, we simply bypass the internal lexicon.

the tagger guesses new tags basing itself on the morphology of the form and its local context.


In order to keep it beyond limits, we only force one form in a sentence at a time to be considered as unknown.

to guess PoS tags for all words in the sentence, the sentence is entirely tagged several times.


However, the scope of the process span an entire corpora and not only one sentence.

considering a large amount of text allows us to compute a statistical ranking of the suspected missing forms which balance the false positives produced by tagging errors.


In order to measure the syntactic coverage of the lexicons at all stages of the merging process, we used the notion of expanded intensional entry [9] which is just a defactorized Alexina subcategorization frame.

each expanded intensional entry describes one fully-specified syntactic behaviour.


For example, given a pair of words A and B, where B would be a verbal predicate, we train a local classifier to assign the label "SBJ:A0", signifying that A is the modifier in a subject dependency relation with its head B, as well as that A is the argument with the A0 role of predicate B.

we merge the class labels of the two tasks into single labels, and present the classifiers with examples with these labels.


The second problem is that since the algorithm classifies the instances in the kernel space, we cannot evaluate the intermediate scores corresponding to the elements of label structures and their features.

most of the available conventional decoding algorithms such as Viterbi cannot be used.


The access to feature vectors does not have locality of reference.

caching support vectors is not efficient.


In this way, if we hold γ for all instances, γ can be updated according to the support vectors newly added to the model.


Since evaluation of each example requires h2 m calls to the kernel function, obtaining one support vector requires h2 m λ calls.

the number of calls to the kernel to obtain H support vectors is, ∑ H h=1 hλ2 m = N I(H + 1)2 m−1 .


For each link, there are 3 possible values: agreement, disagreement, others.


We cannot evaluate this problem precisely by accuracy, since the classes are biased in size.


Since the zero-one cost judges only the overall correctness of the predicted relations, it learns to reserve margin against both near errors and distant errors.

it reserves oversized margin against mistakes of the label elements during learning.


the results are the upper limit that we can obtain by tuning the parameters.


We observed that these distinctions largely depend on the polysemous word processed: the discriminating spaces, when they exist, are not always the same.

we can assume that each space contains different information which is worth being taken into account.


Following [31], we make use of the following statistics: • #(w bg ) -the number of Bulgarian sentences in the bi-text containing the word w bg ; • #(w ru ) -the number of Russian sentences in the bi-text containing the word w ru ; • #(w bg , w ru ) -the number of corresponding sentence pairs in the bi-text containing the word w bg on the Bulgarian side and the word w ru on the Russian side.

nakov & Pacovski [31] tried various combinations of these statistics; in their experiments, the bestperforming formula was the following one: now, note that we have the following inequalities: having a high number of co-occurrences #(w bg , w ru ) should increase the probability that the words w bg and w ru are cognates.


Finally, we define a cross-lingual semantic similarity measure as follows: Note that the above definition has an important drawback: it is zero for all words that do not co-occur in corresponding sentences in the bi-text.

we will never use lex(w bg , w ru ) alone but only in combination with other measures.


However, due to our experiments, this trivial algorithm produces a T with many duplicating titles at the same level in its hierarchical structure and this makes reader have confused.

we must use another learning algorithm to construct T from T i which makes T more coherent, for example, without duplicating titles.


The word cluster in our experiments was derived from the BLLIP corpus that is a general corpus (multidomains).

its could be used without regenerate word clusters.


Owing to these issues, building linguistic resources takes years of constant effort which often fails to achieve visible or useful results.

quick and efficient acquisition and correction of linguistic resources is an unsolved problem of considerable interest to the NLP community.


Manual edition is often error-prone since humans can make typing errors or introduce incoherent data.

collaborative work is generally restricted to a smaller number of skilled persons.


As regards grammatical knowledge, grammars are abstract and static enough to not evolve much.

a grammar designed for French could be used as a starting point to build a grammar for a related language such as Spanish (see sect.


We thus obtain an incremental and sequential way to obtain for both resources corpora representing mostly their shortcomings.

correcting resource A thanks to resource B generate useful data to correct resource B.


We envisage a simple NLG application in which grades obtained by University students in a given course are described as short reports generated automatically from raw data (i.e., the numeric grades themselves) available from their academic records.

the input to our system will be a student's record, and the output is a report conveying a series of statements such as "You fared well in the regular exams and your grades on this subject were above the average of your class" etc.


Our goal in this case is to learn possible rhetorical relations between the sentences produced in the previous stage (which in our data could be either contrast, elaboration or none.)

every related sentence pair in the corpus produced a positive training instance in the form (m 1 , m 2 , relation) in which m 1 and m 2 are messages from one sentence each, and which are found in the nucleus and satellite (or vice-versa) of a inter-sentential rhetorical relation.


Thus, all decision-trees were revised to determine which learning features were actually needed for each classification, and pruned accordingly.

the 14 classification tasks were performed using on average only 2.2 learning features each.


The idea is to use an unlabeled corpus of background knowledge to match unlabeled examples with labeled examples.

if a labeled document A is similar to some document W in the background corpus and an unlabeled document B is similar to the same document W, then perhaps B should have the same label as A.


In addition to labeling papers by topic, we noted which languages were discussed in papers.

we simply labeled a paper with the languages that appear in its text above a certain frequency threshold.


Since we only modeled topics within each distinct field, we must determine which topics of different fields are similar.

we create a topic meta-document for each topic by concatenating the words of every document within the class.


Other polarities interact together with the aim of being saturated.

according to the table, there are two kinds of interactions: • Linear interactions: a linear interaction occurs between exactly one positive feature f → v 1 and one negative feature f ← v 2 to combine in a saturated feature f ; in this way, both features become saturated.


The normalized distance can be defined as: A symmetric version of this cost is then calculated:  The idea in using this measure is that the way phonemes occur together matters less than the way the phonetic features occur together because phonemes themselves are defined in terms of the features.

it makes more sense to a have measure directly in terms of phonetic features.


As previous research has shown [8], even words with a single occurrence on training can turn out to be good predictors on testing.

the classification accuracy for sentiment analysis using machine learning approaches tends to be much lower compared to that for other text classification tasks like topic identification.


This example suggests the idea of amount/measure and Content-Container coexists with Measure, but it is pragmatically inferred from it -since liquids, and especially coffee are usually served in cups.

while the overlap in (5) always holds, the overlaps in (6) and (7) are most of the time resolved by the linguistic context (syntax, semantics) and the context of use (pragmatics).


Each classifier was trained per relation as in the previous experiments, but this time as negative examples we considered only those belonging to the corresponding contingency set.

we split the 1,000 example corpus into three datasets corresponding to the following contingency sets: {Component-Integral, Origin-Entity, Purpose}, {Content-Container, Measure/Content-Container, P-Whp/Measure/Content-Container}, and {P-Whp/Measure, Measure, P-Whp}.


Manual creation of linguistic resources is time-consuming and requires linguistic expertise.

there is a rising interest in developing automatic or semi-automatic methods and techniques for building language resources with minimal human intervention.


the "true" synonyms), and S auto (v i ) be the set of synonyms inferred automatically for v i .

of the automatic process, elements in S auto (v i ) are ranked according to the degree of synonymy they have with v i .


As a result of the automatic process, elements in S auto (v i ) are ranked according to the degree of synonymy they have with v i .

traditional metrics used in information retrieval can be used for evaluating the ranked sets of verb synonyms S auto (v i ) against those in S gold (v i ).


Let a i ∈ A be the set of system's responses for the item t i .

for all test items t i , we have the set of guesses from the system, and the set of responses from the human annotators.


As the next step, the multiset union of the human responses is calculated, and the frequencies of the unique items is noted.

for item t i , we calculate R i , which is r j i , and the individual unique item in R i , say res, will have a frequency associated with it, namely freq res .


Typical implementation makes use of an n×n distance matrix, where n is the number of elements.

the space complexity of the algorithm is O(n 2 ).


To represent the data in machine learning experiments, we use the fact that in Russian and Romanian languages the word sounds directly correspond to the word orthography.

we use the word spelling, a word form, as a substitution for its sound.


In this work, we hypothesize that a word's form and sound have certain relations with its meaning.

the meaning of a word is in part inherited from its form.


On the other hand, words which denote abstract concepts as sensations, feelings and emotions keep more sound semantics in their forms (anger, joy, agitation).

words describing similar sentiments should have something similar in their sounding (vex, worry), whereas words representing opposite feelings should have much less in common (disgust, elation).


We removed duplicate translations as well.

we built six sets of Russian words and six sets of Romanian words expressing the WordNet Affect emotions.


the same letter can represent different sounds depending on the neighboring letters (cat -[k],certain-[s]).

we can conclude that for phonosemantic classification, letter representations may provide relevant information about the word form.


For example, 'tmhrt bEt', which means school, has got a single POS tag, N, in some places and a separate POS tags for each of the tokens in some other places.

unlike [8] who merged a collocation with a single tag, effort has been exerted to annotate collocations consistently by assigning separate POS tags for the individual words in a collocation.


Each segment has an associated DA which defines its dialogue-related meaning (usually the intention, the communicative function, and the important data).

the correct assigment of DAs to a user turn is crucial to the correct behaviour of the dialogue system.


If there is no segmentation available, the search for the optimal DA sequence provides a segmentation that allows the maximum probability to be obtained.

we can obtain a segmentation derived from this method.


In this case, the probability of the sequence U is: To simplify this expression, we do the same simplifications that we did to obtain Equation (3).

the new labelling model is: As in the previous model, we can obtain a segmentation from this Equation.


Note that, in this formula, u k−1 k−n−1 can take DAs of previous turns.

we have derived two labelling models from Equation (1).


The confidence interval for this experiment and confidence interval of the baseline show that the difference between the results given by the models are statistically significant.

it can be concluded that the model  Table 4: Precision, recall and F-measure of the labelling.


Consequently, an ambiguous text might represent several distinct meanings and a concrete meaning might be expressed by different ways.

language ambiguity and variability are considered as essential blocks to solve in order to overcome the barrier that separates the human understanding from the computer understanding.


the Smith-Waterman algorithm vs. word overlap, and WordNet Similarity measures vs. WordNet-based method).

the information gain supplied by the other methods is not enough in order to improve the TE performance.


It is well known that the manual creation of an ontology is a time-consuming and expensive process.

we need to create and extend domain ontologies semi-automatically on the basis of existing resources.


In contrast, in the composition of dictionary definitions, there is usually much more regularity in terms of how synonyms of the word being defined should and would appear.

dictionary definition texts, as a special form of corpora, can provide a better "controlled" environment for synonym distribution and thus, it would presumably be easier to find characteristic features specific to synonymy within definition texts.


In PbE, instead of relating a definiendum to every word in its definition (as in IIE), we focus more on those definientia that follow particular patterns synonyms tend to follow in definition texts.

one of the objectives of PbE is to discover such patterns.


A comparison of the baseline to the sampling experiments shows that the baseline reaches the highest precision and the lowest recall of all experiments.

the experiments corroborate our assumption that instance sampling increases recall while decreasing precision.


A comparison of local sampling and random sampling shows that the former (with a ratio of 1:2.1) results in precision and recall figures that are in the area of random sampling ratio between 1:1.5 and 1:1.75.

while it is considerably more competitive than distance sampling, it reaches results that random sampling reaches with considerably fewer negative examples.


It is only surpassed by the recall value of random sampling with the lowest ratio of 1:1.

despite the very low precision (0.476), this method reaches a competitive F-score of 0.593.


However, due to reading and writing economy, specific terms are usually not often repeated in a document [1].

longer noun phrases are unlikely to be annotated by human annotators, preventing exact matching.


Comparing results from different papers is difficult as no standard datasets are used, and few papers have compared their results on more than one dataset with different competing systems.

it cannot be judged conclusively which approaches improve results on which kind of dataset.


This also prevents influence from varying pre-and postprocessing.

we propose a generalized framework for keyphrase extraction, which allows for fair testing conditions and a comprehensive analysis of results.


Two documents in the DUC2001 data obtained from NIST were empty, and 5 documents had no annotated keyphrases.

the final dataset used in this paper contains 301 documents.


lexical semantic variations (automobile sales, car sales), overlapping phrases (scheduling, real-time scheduling), or morphological variants like plurals (performance metric, performance metrics).

2 we propose a new approximate matching strategy Approx(k gold , k ext ) that accounts for morphological variants (Morph) and the two cases of overlapping phrases: either the extracted keyphrase includes the gold standard keyphrase (Includes) or the extracted keyphrase is a part of the gold standard keyphrase (PartOf).


As no context was given when judging about a matching, annotators were instructed to annotate a pair as invalid if in doubt.

the annotation has a pessimistic bias and rather underestimates human agreement with the approximate matching.


When analyzing the length of the gold standard keyphrases in the training set, we found that -depending on the dataset -97.7 to 99.2% of all keyphrases in the training data contain 1 to 4 tokens.

we limited the length of returned keyphrases to 1 to 4 tokens.


Hence terms belonging to these 3 or 4 classes dominate the results, preventing the remaining classes from being properly represented.

with current methods, taking the top-ranked terms is unlikely to produce a diverse list of terms that is fully representative of the entire domain.


S10 makes no attempt to identify arguments to extracted predicates.

for action awaken, the S10 representation does not distinguish between a case where one entity x is asleep and another entity y wakes up, and the case where x is asleep and then x awakens.


However, a particular event type is likely to occur infrequently in the general news, which might contain many different topics, only a few of which are likely to include mentions of this event type.

a typical evaluation corpus (a few hundred hand-annotated documents), if selected at random, would contain only a few events, which is not sufficient for training.


Third, the category or relevance depends on the annotated data, and a classifier may be unable to deal with articles whose topics were rarely seen in the training data.

if the category distribution of the evaluation data is different from the training data, a text classifier might have poor performance.


Hunting-related or shooting-contest-related activities should not be tagged as Attack events.

the identification of the trigger and the arguments interact: the relation between the trigger and the argument is one essential factor to identify both the trigger and the role of the argument.


For example, if we know that the object of the word "shoot" is a person and it has the "fire a shot" sense, we can confidently identify the person as the Target role, and tag "shoot" as the trigger of an Attack event.

most current event extraction systems consider trigger and argument information together to tag a reportable event (see the baseline system in section 5.1).


IE systems in the real world are often required to work for new domains and new tasks within a limited adaptation or tuning time.

automatic learning of relation extraction rules for a new domain or a new task has been established as a relevant subarea in IE research and development (Muslea, 1999;Tsujii, 2000;Uszkoreit, 2011), in particular for minimally supervised or semi-supervised bootstrapping approaches (e.g., (Brin, 1998;Agichtein and Gravano, 2000;Yangarber, 2001;Sudo et al., 2003;Bunescu and Mooney, 2005;McDonald et al., 2005;Greenwood and Stevenson, 2006;Jones, 2005;Xu, 2007;Kozareva and Hovy, 2010a;Kozareva and Hovy, 2010b)).


The advantage of the minimally supervised approaches for IE rule learning is that only initial seed knowledge is needed.

the adaptation might be limited to substituting the seed examples.


Disagreement between Brill and P50 on a few parts of speech Corpora such as the CoNLL'2000 dataset are expensive to produce and not yet available for many languages.

we were interested in the evolution of performances with the size of the training corpus.


SYM and UH are much more common in a corpus of spoken dialogue transcriptions than in closely edited financial news.

these top four mistakes represent errors arising from the dissimilarity of the domains (as opposed to the fifth mistake, mistaking IN (preposition) for RB, which is a more tra-   Table 3: Results of adding all sentences for which two taggers agree.


Human-robot interactions (HRI) in natural language  pose many challenges for natural language understanding (NLU) systems, for humans expect robots to (1) generate quick responses to their request, which requires all processing to be done in real-time, (2) to rapidly integrate perceptions (e.g., to resolve referents (Brick and Scheutz, 2007)), and (3) to provide backchannel feedback indicating whether they understood an instruction, often before the end of an utterance.

nLU systems on robots must operate incrementally to allow for the construction of meaning that can lead to robot action before an utterance is completed (e.g., a head-turn of the robot to check for an object referred to by the speaker).


Indeed it is not possible to have an abstract syntax, which is common to, and covers all features of all languages.

the current grammar does not cover all aspects of Punjabi.


For instance, when summarizing newswire text, the reader is mainly concerned about the who, what, when, where and why of the fact reported in the news item; when summarizing a research paper, the reader is mostly interested in the problem being faced, the method proposed to solve it and the results achieved.

being capable of knowing what a user would like to read in a summary will allow the summaries to be biased towards such information.


On the other hand, it is important to stress upon the fact that the human written summaries were generated from external sources and written following an abstractive paradigm (i.e., they include material that is not explicitly present in the source documents), whereas our proposed method is an extractive one (i.e., it selects sentences from the source documents).

the chances to have common sentences between our summaries and the human-made ones decrease, as well as the corresponding ROUGE scores.


As already mentioned, the Apertium structural transfer detects sequences of lexical forms which need to be translated together to prevent them from being translated word for word, which would result in an incorrect translation.

adding to the phrase table of a PBSMT system all the bilingual phrase pairs which either match one of these sequences of lexical forms in the structural transfer or an entry in the bilingual dictionary ensures that all the linguistic information of Apertium is encoded with the minimum amount of phrase pairs.


Apertium data has been developed bearing in mind the translation of general texts (mainly news) whereas the in-domain test sets come from the specialised domains of parliament speeches (Spanish-English) or tourism and computer localisation (Breton-French).

as soon as the PBSMT system learns reliable information from the parallel corpus, Apertium phrases become useless.


This may be due to the fact that Moses was trained using in-domain data, i.e., the corpus with subtitles of the same series.

it is more likely that Moses learns specific vocabulary from the series and that translations look more similar to those in the reference set.


In particular, the model has no notion of identity of symbols across the languages!

we transmit objects, or events, e, in the event space E-which is in this case: We do this by means of Bayesian marginal likelihood, or prequential coding, see e.g., (Kontkanen et al., 1996), giving the total code length as: The count c(e) is the number of times event e occurs in a complete alignment of the corpus; in particular, c(# : #) = N occurs as many times as there are word pairs.


A three-dimensional alignment is a triplet of symbols (σ : For example, the words meaning "9" in Finnish, Estonian and Mordva, can be aligned simultaneously as: In 3-D alignment, the input data contains all examples where words in at least two languages are present 5i.e., a word may be missing from one of the languages, (which allows us to utilize more of the data).

we have two types of examples: complete-where all three words present (as "9" above), and incompletecontaining words in only two languages.


Annotators were informed that the annotation was sentence-level and examples of annotated texts presented them were also with annotated sentences.


Sentiment and opinion classification results are highly susceptible to the classification task, the data characteristics and selected text features.

the data characteristics affect the classification accuracy.


People tend to concatenate many clauses with commas.


The Sinica dataset is still not enough to be a moderate training dataset.

we construct a larger Master dataset which is a collection of 1,381 articles written by Chinese masters.


For example, the pair "最初 … 目前" is almost shown in two successive sentences respectively rather than shown in the fragments which belong to a single sentence.

this is a clear cue that a stop should be inserted between inter-sentence connectives.


The 2-tag set, 4-tag set, and 5-tag set schemes are trained over the Master dataset with the feature set on Character Level (i.e., C feature type in Section 4.2).

the 4-tag set scheme outperforms the others.


However, we assumed that a 1 Linguistic Data Consortium (LDC), catalogId: LDC2003T05 model trained on Wikipedia could more effectively identify NEs in Wikipedia (as it is the same domain).

we merged the four NE classes marked in Wiki50 into one NE class to train the CRF with common feature set described above.


multiple occurrences of the same MWE are not taken into account.

in order to compare the results of our approaches to those of mwetoolkit, we assessed our methods similarly to the evaluation scheme used in the mwetoolkit.


From the results of these previous works, we see that several types of features augmented with unlabeled data contribute to improved accuracy.

if we can incorporate new features augmented with unlabeled data, we expect more improved accuracy.


In contrast, to collect rules, we use an automatically tagged data with an NE recognizer.

we expect to obtain more target-task-oriented information with our method than that of previous works.


Such approaches suffered from a proper empirical justification 4 .

machinelearning approaches have replaced manually designed salience measures.


The simple salience based measure is not suited for resolving bridging anaphora.

bridging anaphora was not resolved by the system in these experiments (but still included in the evaluation) which might be a reason for the relatively low recall.


The generation of multi-lingual summaries improves considerably the capabilities of TS systems, allowing users to be able to understand the essence of documents in other languages by only reading their corresponding summaries.

the aim of this paper is to carry out a comparative analysis of several approaches for generating extractive 1 multi-lingual summaries in four languages (English, French, German and Spanish).


The underlying idea was that sentences were placed on different levels of the pyramid, depending on the number of languages containing such sentence.

the top levels were populated by the sentences that appeared in the most languages and the bottom level contained sentences appearing in the least number of languages.


Unlike most extract-based summarization tasks, we are able to automatically generate gold standard summaries for evaluation.

our 1 Several systems for summarizing the opinions expressed in product reviews exist (e.g.


Here, we use the topic coreference resolution approach proposed in Stoyanov and Cardie (2008).

of this step, OA-SIS produces opinion four-tuples [opinion trigger, source, polarity, topic name] that are grouped both  Table 1: Performance of components of the opinion summarization system (Acc.


Opinion set summaries are similar to the output of coreference resolution -both target grouping a set of items together.

our first evaluation metric is based on a popular coreference resolution measure, the B 3 score (Bagga and Baldwin, 1998).


It is not necessarily the case that the last news article on a certain event reports the most up-to-date figures since there is certain latency between reporting on a given event in different countries.


In case of fusing event type information, it turned out that for 5 out of 51 events in our corpus none of the mono-lingual systems was able to assign any type information.

the cross-lingual fusion did not result in any improvement in case of those events, i.e., no type information was assigned.


For instance, the GLOBAL VOICES 12 is a community of bloggers and translators around the globe, who link and translate articles/posts on certain events and issues that are not usually present in international mainstream media.

we plan to carry out experiments on deploying cross-lingual information fusion techniques to refine event extraction from suchlike information sources.


If we expand the equations, each e i e j can be represented by its features, which can potentially be used for determining temporal relation type between events e i and e j .

p(corpus | TC) is rewritten using equation 4.


The EM algorithm includes the following two steps to iteratively estimate the parameters of the model, θ: E-step: Fix current θ and obtain the conditional temporal clustering likelihoods P(TC.| corpus, θ).

for each event pair candidate, a temporal relation type will be selected based on current θ.


In addition, it is difficult to smooth these methods in a desired manner.

we used smoothed relative frequency estimates.


As it is shown in table 2 (in General columns), NONE relations dwarf all other relations.

temporal relation learning, because of heavy bias of learner to NONE relations, will be very hard (even useless).


As distribution of different columns of table 2 shows, the number of NONE relations, even in the intra-sentential case, is about 7 to 10 times greater than other relations.

it is very hard for a learning algorithm to precisely determine the relation types.


On the other hand, both EM 2 and EM 3 showed satisfactory results in these problems.

initialization is a critical factor in our EM method, and some little source of supervision seems crucial for achieving better results.


Building a suitable corpus is a hard, expensive, and time consuming task.

we focused on unsupervised and weakly supervised types of learning.


We have proposed an adaptation of an unsupervised knowledge-based method that combines the original Relevant Semantic Trees method with senses frequency in a voting process.

we have been able to determine which percentage of sense frequency is needed to help the Relevant Semantic Trees method.


Concretely, we decided to apply DLSITE (Ferrández et al., 2007a), a textual entailment recognition system developed by our research group that analyzes pairs of sentences, being one the text and the other the hypothesis, trying to determine if the hypothesis' meaning can be inferred from the text's.

with the use of this system, we could detect plagiarisms that are written in different manners, but still share their meaning.


In our case, rather than constructing the system based on named entity inferences, we study the addition of this knowledge in our textual entailment recognition system.

similarly as we did for verbs, we explored ways to find out entity counterparts between the text and the hypothesis.


In this case we will be able to achieve higher accuracy rates and support a larger number of obfuscation cases.

there is a tradeoff between accuracy and response time, which will be in large determined by the size of the corpus to process.


So, we aim to obtain a high precision of our cognate identification method.

we eliminate false friends and unrelated pairs of words combining statistical techniques and linguistic information (lemmas, POS tags).


LCSR is computed as the ratio between the length of the longest common substring of ordered (and not necessarily contiguous) characters and the length of the longest word.

two words are considered as cognates if LCSR value is greater than or equal to a given threshold.


In our experiments, we use the lemmatized, tagged and annotated parallel corpus described in section 2.

we use lemmas and morphosyntactic properties to improve the lexical alignment.


Sentences containing two company names are extracted.

one hundred sentences are extracted and these sentences are annotated according to the above scheme by two human annotators.


Convolution kernels derive features automatically from complex discrete structures, such as syntactic parse trees or part-of-speech sequences, that are directly provided to the learner.

a classifier can be built without the taking the burden of implementing an explicit feature extraction.


In the previous section, we evaluated predicates often co-occurring with the entire set of protoOHs (Table 1).

we should also check how individual protoOHs or special subsets perform in order to find out whether the simple approach of considering the entire set is the optimal setting.


Despite the undeniable achievements of the research just mentioned, most of it reports the need for a significant amount of human intervention to extract information of existing resources and to represent it in a way that can be compared with another lexicon, or towards proposed standards, such as the mentioned LMF.

there is still room for improvement in reducing human intervention.


Any cost reduction will have a high impact in the actual re-use of resources.

our objective was to reduce human intervention in the first step by devising a blind, semantic preserving mapping algorithm that covers the extraction of the information and the conversion into a format that allows, later, the merging.


Then it is possible to assess that a piece of the code in lexicon A corresponds to a piece of code in lexicon B since a significant number of other verbs hold the same correspondence.

when a correspondence is found, the relevant piece in A will be substituted by the piece in B, performing the conversion into the target format.


The code in Incyta lexicon is as in example (1).

(1) (($SUBJ N1 N0 (FCP 0 INT) (MD-0 IND) (MD-INT SUB)) ($DOBJ N1)) the information that had to be discovered was the following: -Incyta lexicon marks each SCF as a list of parenthesis, where the first level of parenthesis indicates the list of complements.


Whereas words in corpus appear in inflected form, extraction of candidate synsets from our Persian WordNet may not perform properly.

in order to deal with this problem, before beginning our iterative method we performed a shallow stemming process for Persian on corpus.


(2006) has shown that it is possible to mitigate the problem by examining distributional difference of features across different domains, and derive features that are robust against domain switch.

one could expect that applying domain adaptation techniques to authorship attribution can also help with cross-domain authorship attribution.


If this is the case, and if such features are sufficiently informative, then domain adaptation might not be required at all to achieve high performance in domain-independent authorship attribution.

we conduct a comprehensive empirical evaluation using various stylistic features that are likely to be common across different topics and genre.


For training data, we compute tf -iAf based on known authors of each document, however in test data, we do not have access to the authors of each document.

we set tf -iAf of the test data as tf of the test data weighted by iAf of the training data.